Bazinga! Caracterizando e Detectando Sarcasmo e Ironia no Twitter
Resumo
Sarcasmo e ironia são formas de discurso muito utilizadas dentro e fora da Web, tendo o poder de transformar características como polaridade ou sentido de uma sentença. Ser capaz de caracterizar e detectar mensagens sarcásticas ou irônicas em dados coletados da Web pode aprimorar diversos sistemas de tomada de decisão baseados em Processamento de Linguagem Natural (PLN) como a tarefa de análise de sentimentos, sumarização de textos e sistemas de ranqueamento de reviews. Nesse trabalho, propomos diversas abordagens para a caracterização e posterior classificação de sarcasmo e ironia em mensagens postadas na rede social online Twitter. Utilizando uma base automaticamente coletada de tweets com as hashtags #sarcasm" e "#irony", e usando uma larga gama de técnicas de caracterização e classificação, nossos resultados de detecção alcançaram taxas satisfatórias de acurácia e Macro-F1."
Palavras-chave:
Detecção de Sarcasmo, Detecção de Ironia, Twitter
Referências
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Carvalho, P., Sarmento, L., Silva, M. J., and de Oliveira, E. (2009). Clues for detecting irony in usergenerated contents: Oh...!! it’s ”so easy”;-). In Proceedings of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion, TSA ’09, pages 53–56, New York, NY, USA. ACM.
Cha, M., Benevenuto, F., Haddadi, H., and Gummadi, K. (2012). The world of connections and information flow in twitter. In IEEE Transactions on Systems, Man and Cybernetics - Part A.
Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K. P. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. In International AAAI Conference on Weblogs and Social Media (ICWSM).
Cheang, H. S. and Pell, M. D. (2011). Recognizing sarcasm without language: A cross-linguistic study of english and cantonese. page 19.
Gibbs, R. W. and Colston, H. L. (2007). Irony in language and thought: A cognitive science reader. Psychology Press.
Gomide, J., Veloso, A., Jr.,W. M., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatio-temporal locality of twitter. In ACM Web Science Conference (WebSci).
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Hu, M. and Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, pages 168–177, New York, NY, USA. ACM.
Kreuz and Glucksberg (1989a). How to be sarcastic: the echoic reminder theory of verbal irony. Journal of Experimental Psychology: General, pages 374–386.
Kreuz, R. J. and Caucci, G. M. (2007). Lexical influences on the perception of sarcasm. In Proceedings of the Workshop on Computational Approaches to Figurative Language, FigLanguages ’07, pages 1–4, Stroudsburg, PA, USA. Association for Computational Linguistics.
Kreuz, R. J. and Glucksberg, S. (1989b). How to be sarcastic: The echoic reminder theory of verbal irony. Journal of Experimental Psychology: General, 118(4):374.
Lamb, A., Paul, M. J., and Dredze, M. (2013). Separating Fact from Fear: Tracking Flu Infections on Twitter. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 789–795.
Li, G., Ghosh, A., and Veale, T. Constructing a corpus of figurative language for a tweet classification and retrieval task.
Liebrecht, C., Kunneman, F., and van den Bosch, A. (2013). The perfect solution for detecting sarcasm in tweets# not. WASSA 2013, page 29.
Littlestone, N. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. In Machine Learning, pages 285–318.
Popescu, A.-M. and Etzioni, O. (2005). Extracting product features and opinions from reviews. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT ’05, pages 339–346, Stroudsburg, PA, USA. Association for Computational Linguistics.
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes twitter users: real-time event detection by social sensors. In Int’l Conference on World wide web (WWW), pages 851–860.
Singh, R. K. (2012). Humour, irony and satire in literature. pages 65–72.
Tausczik, Y. R. and Pennebaker, J.W. (2010). The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology, 29(1):24–54.
Tsochantaridis, I., Joachims, T., Hofmann, T., and Altun, Y. (2005). Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research (JMLR), 6:1453–1484.
Valitutti, R. (2004). Wordnet-affect: an affective extension of wordnet. In In Proceedings of the 4th International Conference on Language Resources and Evaluation, pages 1083–1086.
Ball, D. W. (1965). Sarcasm as sociation: The rhetoric of interaction. pages 190–198.
BBC. Us secret service seeks twitter sarcasm detector. http://www.bbc.com/news/technology-27711109. Acessado em 12, 2015.
Carvalho, P., Sarmento, L., Silva, M. J., and de Oliveira, E. (2009). Clues for detecting irony in usergenerated contents: Oh...!! it’s ”so easy”;-). In Proceedings of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion, TSA ’09, pages 53–56, New York, NY, USA. ACM.
Cha, M., Benevenuto, F., Haddadi, H., and Gummadi, K. (2012). The world of connections and information flow in twitter. In IEEE Transactions on Systems, Man and Cybernetics - Part A.
Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K. P. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. In International AAAI Conference on Weblogs and Social Media (ICWSM).
Cheang, H. S. and Pell, M. D. (2011). Recognizing sarcasm without language: A cross-linguistic study of english and cantonese. page 19.
Gibbs, R. W. and Colston, H. L. (2007). Irony in language and thought: A cognitive science reader. Psychology Press.
Gomide, J., Veloso, A., Jr.,W. M., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatio-temporal locality of twitter. In ACM Web Science Conference (WebSci).
González-Ibá˜nez, R., Muresan, S., andWacholder, N. (2011). Identifying sarcasm in twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers - Volume 2, HLT ’11, pages 581–586, Stroudsburg, PA, USA. Association for Computational Linguistics.
Hu, M. and Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, pages 168–177, New York, NY, USA. ACM.
Kreuz and Glucksberg (1989a). How to be sarcastic: the echoic reminder theory of verbal irony. Journal of Experimental Psychology: General, pages 374–386.
Kreuz, R. J. and Caucci, G. M. (2007). Lexical influences on the perception of sarcasm. In Proceedings of the Workshop on Computational Approaches to Figurative Language, FigLanguages ’07, pages 1–4, Stroudsburg, PA, USA. Association for Computational Linguistics.
Kreuz, R. J. and Glucksberg, S. (1989b). How to be sarcastic: The echoic reminder theory of verbal irony. Journal of Experimental Psychology: General, 118(4):374.
Lamb, A., Paul, M. J., and Dredze, M. (2013). Separating Fact from Fear: Tracking Flu Infections on Twitter. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 789–795.
Li, G., Ghosh, A., and Veale, T. Constructing a corpus of figurative language for a tweet classification and retrieval task.
Liebrecht, C., Kunneman, F., and van den Bosch, A. (2013). The perfect solution for detecting sarcasm in tweets# not. WASSA 2013, page 29.
Littlestone, N. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. In Machine Learning, pages 285–318.
Popescu, A.-M. and Etzioni, O. (2005). Extracting product features and opinions from reviews. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT ’05, pages 339–346, Stroudsburg, PA, USA. Association for Computational Linguistics.
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes twitter users: real-time event detection by social sensors. In Int’l Conference on World wide web (WWW), pages 851–860.
Singh, R. K. (2012). Humour, irony and satire in literature. pages 65–72.
Tausczik, Y. R. and Pennebaker, J.W. (2010). The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology, 29(1):24–54.
Tsochantaridis, I., Joachims, T., Hofmann, T., and Altun, Y. (2005). Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research (JMLR), 6:1453–1484.
Valitutti, R. (2004). Wordnet-affect: an affective extension of wordnet. In In Proceedings of the 4th International Conference on Language Resources and Evaluation, pages 1083–1086.
Publicado
01/08/2015
Como Citar
GONÇALVES, Pollyanna et al.
Bazinga! Caracterizando e Detectando Sarcasmo e Ironia no Twitter. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 4. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. .
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2015.6778.
